Relaxation methods
have been particularly applied to optimisation
problems. Such problems are very common in
computer vision and so relaxation methods have been
applied in a wide variety of ways to computer vision.

Relaxation methods have been applied to:

Edge linking

-- The
probabilities of edge points lying on particular edges is
determined by considering neighbouring edge points. Different
labels are used for each edge, and a relaxation schedule is then
used to find the appropriate label for each edge point.

Line labelling techniques

can be expressed a relaxation problem.

Label lines
as belonging to a certain class of edge (occluding,
concave or
convex).

Probabilities can be assigned to
each type of
labelling fairly easily.

Only certain sets of
edge labellings are mutually compatible at line junctions.

These
restrictions can be expressed as constraints when the conditional
probabilities for the particular labels are estimated.

Segmentation

can be interpreted in two
slightly different ways here:

The process of grouping pixels into regions of
similarity. Here the relaxation processes amount to
self-organisation of the image. The regions are simply
labelled as .

The process of labelling regions of image as belonging to
recognised physical entities such as sky, grass, trees,
car and road.